{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import rdMolTransforms\n",
    "from rdkit.Chem.Draw import IPythonConsole\n",
    "from psikit import Psikit\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pk = Psikit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pk.read_from_smiles(\"CNS(=O)(=O)CC\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizer: Optimization complete!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-711.7748942789913"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pk.optimize(basis_sets=\"scf/sto-3g\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizer: Optimization complete!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-723.0626131232355"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pk.optimize(basis_sets=\"B3LYP/6-31G*\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+yHxyeXZp6TyZLJ3NFkybluTpedLKSjyG64hEG2fO/NHK6jGF4nRZ2cLu7soBJ9jYzPfy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\ng2CMIoSQQTBGEULIIBijCCFkkP8HNyhcpre+9QsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7f6804111ce0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pk.mol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['C',\n",
       " 'N',\n",
       " 'S',\n",
       " 'O',\n",
       " 'O',\n",
       " 'C',\n",
       " 'C',\n",
       " 'H',\n",
       " 'H',\n",
       " 'H',\n",
       " 'H',\n",
       " 'H',\n",
       " 'H',\n",
       " 'H',\n",
       " 'H',\n",
       " 'H']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[a.GetSymbol() for a in pk.mol.GetAtoms()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conformer = pk.mol.GetConformer(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 -723.056921763491\n",
      "10 -723.0574009035583\n",
      "20 -723.0583845883023\n",
      "30 -723.0595485772963\n",
      "40 -723.0605034078015\n",
      "50 -723.0609938370956\n",
      "60 -723.0608832849458\n",
      "70 -723.0600488840882\n",
      "80 -723.0585385255836\n",
      "90 -723.0567086747076\n",
      "100 -723.0551682176562\n",
      "110 -723.054408816703\n",
      "120 -723.0545318226641\n",
      "130 -723.0554506590202\n",
      "140 -723.0570179572535\n",
      "150 -723.0589412650767\n",
      "160 -723.0607819309749\n",
      "170 -723.0620693038213\n",
      "180 -723.0625640298554\n",
      "190 -723.062178569547\n",
      "200 -723.0611117138934\n",
      "210 -723.0596917366915\n",
      "220 -723.0583669682219\n",
      "230 -723.0574853343633\n",
      "240 -723.0572859520408\n",
      "250 -723.0577599957417\n",
      "260 -723.058744956759\n",
      "270 -723.0599360174974\n",
      "280 -723.0610146943501\n",
      "290 -723.061727957017\n",
      "300 -723.0618940554882\n",
      "310 -723.0614391811523\n",
      "320 -723.0604675894782\n",
      "330 -723.0592105713769\n",
      "340 -723.0579701713469\n",
      "350 -723.0571320334551\n"
     ]
    }
   ],
   "source": [
    "dihedral_energies = []\n",
    "for dihedral_degree in range(0, 360, 10):\n",
    "    rdMolTransforms.SetDihedralDeg(conformer, 3, 2, 5, 6, dihedral_degree)\n",
    "    degy = pk.energy(basis_sets=\"b3lyp/6-31g*\")\n",
    "    print(dihedral_degree, degy)\n",
    "    dihedral_energies.append(degy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "de = np.array(dihedral_energies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "de_kcal = (de - de.min()) * 627.51 # convert hartree -> kcal/mol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.54057857, 3.23991338, 2.62264137, 1.89222664, 1.29306094,\n",
       "       0.98531166, 1.05468424, 1.57827912, 2.52604419, 3.67429391,\n",
       "       4.64094611, 5.11747781, 5.04029033, 4.46371133, 3.48021602,\n",
       "       2.27332113, 1.11828487, 0.31044553, 0.        , 0.2418802 ,\n",
       "       0.91134279, 1.80239268, 2.63369815, 3.18693221, 3.31204661,\n",
       "       3.01457945, 2.39650656, 1.64910403, 0.97222352, 0.52464407,\n",
       "       0.42041562, 0.70585381, 1.3155373 , 2.10432873, 2.88269215,\n",
       "       3.40863206])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "de_kcal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tps = np.array(range(0, 360, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\"timepoint\":tps, \"relative_energy\":de_kcal})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RERGR1Kx6NbZ//34EBgYiNTUVJ06cMDu/paXLmuEQkQXqW7qw+0S5OF4+YxzaW7vRLmFM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\nMhkWZvQXLjl+vhZdPToTzyC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t/vF53qBk7Yfqr3TbMCL7YPY3LDU1FQBQU1OD6dOnD/pT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\nYPFGRERERERkBv4fiP7PPdppBvQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={\"figure.figsize\":(15,5)})\n",
    "g = sns.pointplot(x=\"timepoint\", y=\"relative_energy\",data=df)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
